Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
arXiv preprint arXiv:2502.03609 , year=
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A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.
Conformalized quantile regression applied post hoc to neutron star posterior samples yields reliable uncertainty bands validated by empirical coverage studies.
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Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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A new kernel nonconformity score for multivariate conformal prediction that adapts to residual geometry, provides finite-sample coverage, and achieves convergence rates based on effective kernel rank rather than ambient dimension.
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Conformalized quantile regression applied post hoc to neutron star posterior samples yields reliable uncertainty bands validated by empirical coverage studies.